System and method for generating influencer scores

ABSTRACT

Methods and systems for generating influence scores are disclosed. A plurality of opinions of a selected topic are retrieved from a database. Influencers each associated with at least one opinion from the plurality of opinions are determined using the processor. A score is automatically generated for each influencer using the processor, the score related to the selected topic.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication Ser. No. 61/704,269, filed on 21 Sep. 2012, which isincorporated herein by reference in its entirety.

FIELD

The present disclosure relates to methods and systems for generatinginfluence scores. More particularly, the present disclosure relates toan influence engine that collects opinions from influencers andcalculates influence scores indicative of the influence of theparticular influencer on a particular topic.

BACKGROUND OF THE INVENTION

With the explosion on the internet of news outlets, social network sitesincluding Facebook and Twitter, and personal platforms such as blogs, ithas become increasingly difficult to track all the narratives,conversations, and interactions proffered by these data sources. Theoverwhelming amount of sources of information and chatter provided bythe aforementioned sources such as Facebook, Twitter, and blogsnecessitates the identification of influencers and the determination ofexactly how much influence their actions may have on the public at largefor a variety of topics. An influencer may be any individual whointeracts with other individuals in the context of aconversation—including publishers, authors, broadcasters, persons,organizations, bloggers, commenters, and social media users generatingor participating in such conversations. President Obama's influence mayarise by virtue of quotations in news media, mentions in blogs, andcontent generated by his @BarackObama twitter account. However, it istoo time consuming to manually wade through all of the conversations toattempt to determine which participants and sources are most highlyinfluential. A more efficient solution is therefore needed to determineinfluence levels of influencers.

SUMMARY

The present disclosure relates to methods and systems for generatinginfluence scores. More particularly, the present disclosure relates toan influence engine that collects data from influencers and calculatesinfluence scores indicative of the influence of the particularinfluencer on a particular topic. An influencer is scored based on theinteractions with those individuals he has influenced on the giventopic. When an individual exerts influence across multiple networks ofinteraction, the scores are combined based on equivalence relationsembodied by a mapping service. For example, the named entity BarackObama might be influential in news articles, while user @BarackObamamight be influential in Twitter. When the mapping service indicates thatthese personas represent the same individual, the scores are combined toyield a single composite influencer.

A mapping service in accordance with the embodiments described hereinmay be an automatic system identifying influencers as the same entities.For example, Barack Obama may be an influencer extracted from Newsarticle and @BarackObama may be an influencer based on a Twitter handle.A mapping service identifies both influencers as the same entity (commoninfluencer). The influencer engine will calculate one unified influencescore for Barack Obama based on the foregoing. Each influencer is anentity, and each entity may have different names existing in differentor the same data sources.

In an embodiment, a method for determining influence scores isdisclosed. Opinions of a selected topic are retrieved. Influencers, eachassociated with at least one opinion on the selected topic, aredetermined based on at least one parameter. A score is generated foreach influencer with respect to the selected topic.

In another embodiment, retrieving opinions of a selected topic comprisesextracting opinions from data sources. The data sources may comprise atleast one of: news sites, blogs, forums, transcripts, tweets, comments,quotes, and social network entries.

In another embodiment, determining influencers comprises: analyzing theopinions to determine those associated with at least one influencer, andassociating the opinions of the at least one influencer with a commoninfluencer.

In another embodiment, generating a score for each influencer comprises:determining interactions by each influencer with other entities;calculating an interaction factor for each influencer based on theinteractions; and generating the score for each influencer based on thecalculated interaction factor.

In another embodiment, the score is based on a weighted score of eachinfluencer with respect to a data source.

In another embodiment, the selected topic including a set of constraintsis received.

In another embodiment, a list of influencers including the correspondingscore of each influencer with respect to the selected topic is provided.

In an embodiment, a system for determining influence scores isdisclosed. The system may comprise an influence engine. The influenceengine comprises an opinion unit configured for retrieving opinions on aselected topic; an influencer mapping unit configured for determininginfluencers each associated with at least one opinion based on at leastone threshold parameter and a score generation unit configured forgenerating a score for each influencer, the score related to theselected topic.

In another embodiment, the system further comprises an opinionextraction engine configured for extracting opinions from data sources,wherein the extracted opinions may be provided to the opinion unit. Thedata sources may comprise at least one of: new sites, blogs, tweets,comments, quotes, transcripts, and social network entries.

In another embodiment, the influencer mapping unit is further configuredfor: analyzing the opinions to determine those associated with at leastone influencer, and associating the opinions of the at least oneinfluencer with a common influencer.

In another embodiment, the score generation unit is further configuredfor determining interactions by each influencer with other entities;calculating an interaction factor for each influencer based on theinteractions; and generating the score for each influencer based on thecalculated interaction factor.

In another embodiment, the score is based on the weighted score of eachinfluencer with respect to a data source.

In another embodiment, the system comprises a user interface unitconfigured for receiving the selected topic including a set ofconstraints.

In another embodiment, the system comprises a user interface unitconfigured for providing a list of each influencer along with thecorresponding score of each influencer with respect to the selectedtopic.

In an embodiment, a machine-readable non-transitory and tangible mediumhaving information recorded thereon for generating influence scores isdisclosed. The information, when read by the machine, causes the machineto perform the steps comprising: retrieving opinions of a selectedtopic; determining influencers each associated with at least one opinionbased on at last one threshold parameter; and generating a score foreach influencer, the score related to the selected topic and generatedaccording to the algorithm or formula recorded on the supplied medium.

In another embodiment, retrieving opinions of a selected topic comprisesextracting opinions from data sources. The data sources may comprise atleast one of: news sites, blogs, forums, transcripts, tweets, comments,quotes, and social network entries.

In another embodiment, determining influencers comprises: analyzing theopinions to determine those associated with an influencer, identifyingat least two influencers that are the same entity; and associating theopinions of the at least two influencers with a common influencer thatrepresents the at least two influencers.

In another embodiment, determining influencers comprises: analyzing theopinions to determine those associated with at least one influencer, andassociating the opinions of the at least one influencer with a commoninfluencer.

In another embodiment, generating a score for each influencer comprises:determining interactions by each influencer with other entities;calculating an interaction factor for each influencer based on theinteractions; and generating the score for each influencer based on thecalculated interaction factor.

In another embodiment, the score is based on a weighted score of eachinfluencer with respect to a data source.

In another embodiment, the selected topic including a set of constraintsis received.

In another embodiment, a list of each influencer including thecorresponding score of each influencer with respect to the selectedtopic is provided.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe disclosed embodiments. The advantages of the present embodiments maybe realized and attained by practice or use of various aspects of themethodologies, instrumentalities and combinations set forth in thedetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments described herein may be described in detailwith reference to the drawings. These embodiments are non-limitingexemplary embodiments, in which like reference numerals representsimilar structures throughout the several views of the drawings.

FIG. 1 illustrates an exemplary system for generating influence scores,in accordance with an embodiment of the present disclosure.

FIG. 2 illustrates inputs and outputs of an exemplary opinion extractionengine, in accordance with an embodiment of the present disclosure.

FIG. 3 illustrates inputs and outputs of an exemplary influence engine,in accordance with an embodiment of the present disclosure.

FIG. 4 illustrates an exemplary mapping of an influencer with variousdata or opinion sources, in accordance with an embodiment of the presentdisclosure.

FIG. 5 illustrates an exemplary process for generating influence scores,in accordance with an embodiment of the present disclosure.

FIG. 6 illustrates an exemplary process for generating influence scores,in accordance with an embodiment of the present disclosure.

FIG. 7 illustrates an exemplary influence engine, in accordance with anembodiment of the present disclosure.

FIG. 8 illustrates a general machine architecture on which the presentembodiments can be implemented and has a functional block diagramillustration of a hardware platform which includes user interfaceelements.

DETAILED DESCRIPTION

The present disclosure and embodiments described herein provide a systemthat determines influencers and calculates an influence score for eachinfluencer based on a given set of opinions associated with eachinfluencer. An influencer may be defined as one who holds an opinion orgives an opinion. For example, an influencer may be a celebrity who hasgiven their opinion on the state of innovation in the United States,where the opinion is published by a news website. Opinions used as thebasis for calculating influence scores are selected based on a topicthat may be predetermined or selected, and query or topic constraints.The topic may represent any topic of interest, such as consumer goods,political issues, current events issues, brands, and other topics thatmay be of interest due to a high number of opinions by variousindividuals or entities. The query constraints may be, for example,keywords that define the topic. If a topic is, for example, “dippingsauces”, the topic may be further refined by a query constraint such asa particular brand name that sells or manufactures dipping sauces.

The opinions themselves are extracted by an opinion extraction enginewhich is responsible for retrieving content from data sources, such asnews sites, blogs, forums, transcripts, tweets, comments, quotes, andsocial network entries, and any other archived content on the Internetthat may include opinions. The opinion extraction engine analyzes thedata sources and extracts opinions from the data sources, as well asassigning attributes to the opinions. The opinion attributes mayinclude, for example, an opinion holder, an author, a publisher, and apublish date. For example, for a particular data source, an opinionholder or influencer may be President Obama, an author may be anewspaper editor, a publisher may be the newspaper, such as the New YorkTimes, and the publish date indicates the date of publication. In atweet, an opinion holder may be a twitter username whose opinion ismentioned or retweeted.

Each influencer's influence is measured across data sources. Thus, asingle influencer's influence score is based on a sum or a weighted sumof their influence scores for each data source. For example, if opinionsfor an influencer are found on multiple data sources, such as multiplenews websites, a blog, and tweets, the influence score for thatinfluencer will be based on subscores for each of the individual datasources. Furthermore, an influencer mapping system automatically mergesthe scores for influencers which are determined to actually be a singleentity or person. If there are two separate scores for, one forPresident Obama and one for Barack Obama, the system is able todetermine that these two people are the same and thus scores for bothPresident Obama and Barack Obama will be automatically combined as thetwo people or entities are mapped to each other. The influencer mappingfeature determines that multiple influencers are actually the sameinfluencer by using contextual attributes such as commonality in names,opinions, dates, related entities, etc. Thus, one unified influencescore will be calculated for President Obama and Barack Obama.

FIG. 1 illustrates an exemplary system for generating influence scores,in accordance with an embodiment of the present disclosure. System 100as shown, includes user devices 102, content or data sources 104,opinion extraction engine 106, and influence engine 110. Communicationmay take place over network 112. Several user devices 102 a, 102 b, and102 c are shown to demonstrate that the user device may be a mobilephone, mobile computing device, or a traditional computing device orserver based system. Furthermore, a user device may include mobiledevices such as smartphones, tablets, and traditional computing devicessuch as desktop computers. A user device may also comprise any devicecapable of communication over the Internet through network 112, and thusmay be inclusive of vehicles or other equipment or apparatus supportingcommunication with content sources 104, influence engine 110, or opinionextraction engine 106 through a user interface or without a userinterface. Other user devices suitable for use in accordance with theembodiments described herein, although not shown, may also take theplace of the user devices shown. While only one influence engine 110 andopinion extraction engine 106 are shown, it is understood that thesystem may comprise a plurality of influence engines and opinionextraction engines as needed.

Content or data sources 104 includes any data source from which opinionextraction engine 106 retrieves content to extract opinions. Thus, thecontent sources may include news websites, blog sites, Twitter, socialnetwork sites, and any other web based site that may include opinions.Opinion extraction engine 106 retrieves content from content sources 104and analyzes the content to extract opinions. The extracted opinions maythereafter be provided to influence engine 110.

Influence engine 110 retrieves opinions from opinion extraction engine106. Opinions may be retrieved based on a user device 102 selecting atopic. Based on this selection, influence engine 110 will retrieveopinions from opinion extraction engine 106 regarding that particularselected topic. The selected topic represents a topic that a user ofuser device 102 may be interested in. Accompanying the topic selectionmay also be query constraints, such as keywords which further refine theamount or sources of data from which the opinions may be retrieved.

Influence engine 110 thereafter determines influencers by analyzing theretrieved opinions in an attempt to determine those that are actuallyassociated with an influencer. In the event that at least twoinfluencers are identified that may both represent the same entity orperson, the opinions of the at least two influencers are associated. Theat least two influencers may thus be designated as a common influencerand all opinions of the at least two influencers may be mapped to thecommon influencer. As stated in the example above, President Obama andBarack Obama are two influencers that are actually the same entity orperson. Thus, influence engine 110, by way of a mapping feature, mapsthe opinions of President Obama and Barack Obama to either one of theentity names President Obama or Barack Obama.

Determination of influencers may be based upon threshold parameters thatdictate how an entity, individual, or opinion holder is qualified orcategorized as an influencer. For news sources and blogs, an opinionholder may be deemed an influencer based on whether the opinion holdermeets a threshold parameter for number of opinions in a certain timeframe. For instance, to be an influencer, the opinion holder must havemore than two opinions published in the last two months or 60 days.

For tweets, an influencer may be determined based on having at least oneinteraction defined as a reply, retweet, or mention. For example, anindividual with a first twitter handle may send a tweet that isretweeted or replied to by a second twitter handle. Thus, the secondtwitter handle is influenced by the first twitter handle and the firsttwitter handle is deemed an influencer. In the instance of mentions, theinfluencer is not the one who holds the opinion. Instead, the influenceris determined based on what is mentioned. If a tweet states: I like@Ford's car, Ford is mentioned and is the influencer.

Influence engine 110 generates influence scores by using an algorithmthat may produce a number indicative of the influencer. This number isthe influence score and represents the amount of influence theinfluencer has with regard to the selected topic. The influence scoremay be calculated using variations of the following equation (1):

$\begin{matrix}{{{score}\left( {influencer}_{k} \right)} = {\sum\limits_{j}^{J}\; {{{score}\left( {influencer}_{j} \right)}*{{interaction}\left( {k,j} \right)}}}} & (1)\end{matrix}$

The variable k represents a number associated with a particularinfluencer. The variable j represents a number associated with aninfluencer which influencer_(k) interacts with. Interaction(k,j) denotesan interaction between influencer_(k) and influencer_(j). For differentdata sources, the interaction can be different. For example, in twitter,an interaction may be a retweet, reply, or mention. In news, theinteraction may be an opinion quoted in a news article by the author.Influence engine 110 will decide which equation to use by reading thedata source of opinions. Thus the influence score for an individual isbased upon an interaction factor representing interactions of theinfluencer with other opinion holders or influencers. The reasoningbehind the interaction factor is that interaction with more highlyinfluential individuals yields higher influence For example, aninfluencer could have a higher score by virtue of quotation by aprominent publisher or retweet by an influential twitter user.

Influence scores may also be based upon a weighted sub-score of theinfluencer with respect to a particular data source. Influence engine110 is aware of all of the data sources from which the opinions wereobtained and thus can weigh certain data sources more heavily incalculating influence in the event that a particular data source is morecredible, or is experiencing a renaissance period with a rise in viewership or subscriptions. Thus, the influence scores may be based oncertain factors such as the number of days over which opinions arecollected by opinion extraction engine 106, total number of times apublisher has quoted an opinion of an influencer, and readership size ofthe publisher.

For example, if the data source is news websites or blogs, the influencescore may be calculated in a certain manner weighing each data source.In the case of news websites or blogs, the influencer may be defined asone who holds an opinion, an influencee as the publisher, and aninteraction as the publisher quoting the opinion holder. For example, ina quote from a New York Times article: “President Obama emphasized theimportance of American innovation,” the influencer is President Obama,the publisher New York Times, and the interaction between the New YorkTimes and President Obama. An influence score may be calculated usingthe following equation (2):

$\begin{matrix}{{{score}\left( {influencer}_{k} \right)} = {\sum\limits_{d}^{D}{\sum\limits_{p}^{P}{\sum\limits_{opinion}^{{{opinion}{({p,k})}}}{\left( {tier}_{p} \right)/{D}}}}}} & (2)\end{matrix}$

Publisher, as used herein, refers to the publisher of a news website orblog, such as the New York Times, or Huffington Post. Opinions arecollected by opinion extraction engine 106 over D days. P denotes apublisher P of a set of publishers. tier_(p) represents a publisher P'stier, which is determined based on the publisher's readership size. Sixtiers exist, valued from 0 through 5 to represent the influence factoran influencer receives from a publisher tier. A higher value isindicative of a larger readership size. For example, very well knownpublishers such as ABC and CNN may be in tier 5, and lesser knownpublishers such as Daily Sports Updates may be in tier 0. opinion(p,k)opinion denotes the set of opinions of influencer_(K) published by p.Example: Suppose that over the past D days, Barack Obama has 2 opinionsabout “innovation” published by The New York Times at tier_(NYT) and 1opinion published by Bob's Politics Blog at tier_(BPB). Then Obama'sinfluence score on “innovation” is (2*tier_(NYT)+1*tier_(BPB))/D.

Publisher diversity may also be taken into account in calculatinginfluence scores. The goal of using publisher diversity is to lowerinfluence scores for influencers who are quoted repeatedly by the samesingle publisher. Thus, equation (2) may be modified and the influencescore calculated using the following equation (3):

$\begin{matrix}{{{score}\left( {influencer}_{k} \right)} = {\sum\limits_{d}^{D}{\sum\limits_{p}^{P}{\sum\limits_{{opinion}_{i = 1}}^{{{opinion}{({p,k})}}}{{\alpha^{i - 1}\left( {tier}_{p} \right)}/{D}}}}}} & (3)\end{matrix}$

α^(i) represents a penalty factor for having multiple opinions from anysingle publisher. We use 0.85 for α. For example, if two opinions fromthe same publisher, then the second opinion will only get 0.85 of thescore from first opinion since 0.85⁽²⁻¹⁾ is 0.85.

As another example of how an influence score is calculated, PresidentObama may have 6 associated opinions published by ABC, New York Times,Yahoo! News, two personal blogs, and a local news website. PresidentObama's score is calculated in accordance with the embodiments describedherein by the weighted sum of a sub-score of representing the influencescore of President Obama with respect to each publisher or data source.As discussed, the tier of the publisher is taken into account, and thus,President Obama's influence score will be significantly weighted towardsthe sub-scores from the higher tier publishers.

Influence scores may also be calculated differently for opinions of aninfluencer that are extracted from Twitter, or other social networksites such as Facebook, G+, or other similar social network sitesallowing one to express their opinion. The algorithm used to calculatean influence score based on opinions from Twitter or other socialnetwork sites is based on the probability that one influencer isinfluenced by another influencer. For example, the probability that oneTwitter user is influenced by another Twitter user. Especially in thecase of Twitter, citation events and interactions are in the form of“retweets”, “mentions”, and “replies” between different users ofTwitter. Thus, the algorithm captures the probability that an individualtraversing the network will arrive at a particular influencer byfollowing chains of citation, such as retweets, mentions, and replies.Any influencer who is cited more often, by other more influentialinfluencers, will have a higher influence score. Thus, each influencerthat cites another, distributes their own influence score to thosecited, thus increasing the score of the person cited.

Thus, for opinions from social networks, and for Twitter in particular,an influencer may be defined as the individual who expresses an opinionthat is mentioned, replied to, or retweeted or forwarded. An influenceeis defined as the one who mentions, replies, retweets or forwards anopinion of the influencer. The interaction for social networks is theactual retweet or forward, mention, or reply. For example, if a Twitteruser with handle CrazyMan posts on Twitter: “I like @BarackObama's ideaabout supporting innovation”, CrazyMan is the influencee, and@BarackObama is the influencer.

An influence score may be calculated using the following equation (4):

$\begin{matrix}{{{score}\left( {influencer}_{k} \right)} = {\left( {1 - d} \right) + {d*{\sum\limits_{g}^{{interaction}{({g,k})}}\; {{{score}\left( {influencer}_{g} \right)}/{out}}}} - {{degree}\left( {influencer}_{g} \right)}}} & (4)\end{matrix}$

In equation 4, influencer_(k) denotes an influencer we want to measurethe score for. score(influencer_(g)) is the score of influencer_(g).out-degree(influencer_(g)) is the frequency of retweets, mentions, andreplies of influencer_(g). The |interaction(g,k)| denoted as the totalnumber of interactions occurring between g and k. d is a damping factor.The purpose of the damping factor is to reflect the probabilistic decayof influence through interactions. Influence scores can be calculatedusing equation 4 iteratively. Firstly, we need to get the out-degree andin-degree information for each influencer. In the initial iteration, weassign all influence score the same score as uniform initial scores. Ineach iteration of score calculation, the score for each influencer isrecalculated from the sum of the scores of individuals citing theinfluencer. Once a convergence is reached where the scores arestabilized between iterations, the algorithm ends, and the resultinginfluence score associated with the influencer is used as the score. Forexample, @BarackObama's influence score is calculated from the sum ofthe scores of the influencees who retweet/mention/reply to @BarackObamanormalized by the number of influencees.

As discussed above, influence engine 110 is also configured to invoke amapping feature to identify multiple influencers as the same commoninfluencer. Based on this identification, a merged influence score maybe calculated. Calculation of merged scores may be carried out using thefollowing equation (5):

$\begin{matrix}{{{score}\left( {influencer}_{k} \right)} = {{\max (S)} + {\sum\limits_{{s = i},{i \neq {{argmax}{(S)}}}}^{S}\; {s_{i}/{S}^{\beta}}}}} & (5)\end{matrix}$

max(S) is the highest score of all of the scores from influencersidentified as the same person. β is a fixed weighting factor thatdetermines how much score an influencer may receive from non-majoritynetworks. |S| is the number of networks. For example, if Barack Obamahas an influence score of 100 on the topic of healthcare solely based onnews websites and blogs, and Mitt Romney has scores of 70 from newswebsites and blogs and 60 from tweets, it is inaccurate to say that MittRomney is more influential than Obama by summing 70+60 simply becauseRomney holds a Twitter account and has opinions. However, Mitt Romney'sextra outlets for opinions should be taken into account.

The examples shown below demonstrate how merged scores are determined.In the examples, shown below, a β of 2 is assigned.

Example 1

Twitter based influence score: 100, News website based influence score:50, Social Network based influence score: 70.

Total # of Networks: 3 Maximum Score: 100

Merged influence score=100+(50+70)/3̂2=113.

Example 2

Twitter based influence score: 2, News website based influence score: 30

Total # of Networks: 2 Maximum Score: 30

Merged influence score=30+(2/2̂2)=30.5

Example 3

Twitter based influence score: 100, News website based influence score:100

Total # of Networks: 3 Maximum Score: 100

Merged influence score: 100+(100)/2̂2=125

Once influence engine 110 has generated a score for each individualbased on the selected topic, all of the influence scores may be providedto the user that selected the topic. The influence scores may beprovided as a list including corresponding influencer details andinfluence scores. The user may be any user that may utilize theinfluence scores to determine which influencers are most influentialwith respect to the selected topic.

Influence gap score is a score representing the total influence of atopic. Influence gap score can be calculated by equation 6.

${{{influencegapscore}(t)} = {\sum\limits_{s}^{S}\; s}},{s \in t}$

t is a topic, s is an influence score from topic t. |S| denotes as thetotal number of influence scores in topic t. One usage of the influencegap score is to help people monitor and compare the influence activitiesof their topics different over time or with other topics. For example, acompany might define a topic to track one of its products. It can thenreact appropriately to changes in the product's influence gap score overtime, perhaps by targeting appropriate influencers or sources withadditional messaging.

For example, if a company wanted to ascertain their brand awareness, itwould be useful for the company to determine key influencers that eitherhad many positive or many negative opinions about the company. With thisinformation, the company could formulate new strategies for brandawareness and also know which individuals or types of individuals totarget.

Influence engine 110 may host a user interface that can be provided to auser device 102 in order to provide the influence scores andcorresponding influencers to the user of user device 102. Additionally,the user interface may provide additional functionality that allows theuser of user device 102 to set their selected topic from which opinionsare gathered to determine the influencers and calculate the influencescores, and also provide additional features allowing a user of userdevice 102 to ascertain more information about certain influencers.

Although not shown, any number of opinion extraction engines 106 andinfluence engines 110 may be a part of a backend of any web servercapable of communication across a network such as network 112 tocommunicate with user devices 102 and content sources 104. Thus, in thisembodiment, any communication with the opinion extraction engine 106 andinfluence engine 110 is via the web server that provides the services ofopinion extraction engine 106 and influence engine 110 on its backend.

FIG. 2 illustrates inputs and outputs of an exemplary opinion extractionengine, in accordance with an embodiment of the present disclosure.Opinion extraction engine 106, as shown receives as input, content ordata from content sources 104. As described above, this content isanalyzed such that opinions are extracted Various attributes are alsoextracted, such as opinion holder, author, publisher, and publish dateof each opinion. The opinions extracted may then be provided as outputto influence engine 110.

FIG. 3 illustrates inputs and outputs of an exemplary influence engine,in accordance with an embodiment of the present disclosure. Influenceengine 110, as shown, receives as input, the opinions of influencersthat are extracted by opinion extraction engine 106, as well as aselected topic of opinions to be used as a basis for generatinginfluence scores. Influence engine 110 uses the opinions and theselected topic to first determine influencers, and then calculateinfluence scores for each influencer, as described above. The outputfrom influence engine 110 comprises influencer names that are associatedwith various opinions, and influence scores that are calculated for eachinfluencer. In certain circumstances, multiple influencers may representthe same common influencer, and thus, influence engine 110 will map thescores of the multiple influencers to the same influencer.

FIG. 4 illustrates an exemplary mapping of an influencer with variousdata or opinion sources, in accordance with an embodiment of the presentdisclosure. FIG. 4 illustrates an opinion holder, Obama 402, whichrepresents an influencer. The other circles all represent possible datasources from which opinions can be gathered. Circle 404 represents ABCwhich may be a news website, Circle 406 represents a personal blog,circle 408 represents a local news website, circle 410 represents theNew York Times, circle 412 represents Yahoo! News, and circle 414represents another personal blog. While the embodiments described hereinhave been described with respect to data or content sources that areaccessed through the Internet, the data or content sources may alsoinclude traditional print media sources to the extent that any of thecontent from these traditional media sources has been archived oruploaded such that they are accessible through the Internet. Forexample, the New York Times website may contain an archive ofpublications from the 1990s which were not previously provided on thewebsite. However, as these publications are archived and accessiblethrough the Internet, any opinions from these archived publications maybe retrieved by opinion extraction engine 106 to extract opinions toprovide them to influence engine 110.

FIG. 5 illustrates an exemplary process for generating influence scores,in accordance with an embodiment of the present disclosure. Theexemplary process illustrated by FIG. 5 may be carried out by influenceengine 110. Influence engine 110 may be a component of a computer or aprocessor. The processor may be coupled to a display and storage. At502, opinions of a selected topic are retrieved. The opinions may beretrieved from an opinion extraction engine 106 based on the selectedtopic. Opinion extraction engine 106, like influence engine 110, mayalso be a computer or a processor. The topic may be selected by a user,and the topic selection can be accompanied by a set of constraints thatfurther define the selected topic. For example, a selected topic may bevacation rentals, and an accompanying constraint may be the region ofNorth America, and thus only opinions regarding North American vacationrentals will be retrieved. The opinions are extracted from data sourcessuch as news sites, blogs, tweets, comments, quotes, and social networkentries from social network sites. Selection of the topic may take placevia a user interface that provides a display allowing entry of thetopic. Entry of the topic results in storage of information related tothe topic in a database which may subsequently be retrieved by influenceengine 110 or opinion extraction engine 110 to retrieve opinions.

At 504, influencers associated with the opinions are determined.Determination of influencers may be performed by influence engine 110 byanalyzing the opinions to determine which are associated with particularinfluencers. In the event that at least two influencers are identifiedthat may be the same entity or common influencer, the opinions of theseinfluencers are mapped to the common influencer so that when theinfluence score is calculated, there will be a single merged influencescore.

At 506, a score is generated for each influencer. Generation of thescore may be based on interactions by each influencer with associatedentities. Thus, these interactions must be determined. An example of aninteraction is an influencer who interacts with a particular newswebsite or blog to provide an opinion. Thus, the higher the quality andnumber of interactions between the influencer and the influencee, thehigher the score of the influencer. The influencee itself may on someoccasions have a score that is generated, and thus this is also takeninto account in calculating the score. Calculation of the influencescore is also based on a weighted score of each influencer with respectto a data source. Weighting of scores is based on what the particulardata or content source is, for example, new websites or blogs or tweetsfrom users of Twitter. This results in calculations of sub-scores for aparticular influencer based on each different content source withopinions for the influencer. These sub-scores are then summed togetherbased on a weighting of each sub-score.

At 508, a list of influence scores with corresponding influencer namesis provided to a user of a device through a user interface. The userinterface additionally may allow the user to select topics and setconstraints for influence engine 110 to generate influence scores. Theuser interface may also be configured to provide additional informationto the user about particular influencers and present the list ofinfluence scores in different formats at the direction of the user. Theuser interface may reside at a user machine or reside at a machinenetworked with influence engine 110 and be accessible from a usermachine. The list of influence scores and influencer names may be storedat a database for retrieval via the user interface based on theaforementioned steps of the process illustrated by FIG. 5 which arecarried out by influence engine 110, or a computer or a processorembodying influence engine 110.

FIG. 6 illustrates an exemplary process for generating influence scores,in accordance with an embodiment of the present disclosure. At 602,interactions of each influencer with different entities is determined byinfluence engine 110. Influence engine 110 may be a component of acomputer or a processor. The processor may be coupled to a display andstorage. The other entities may be other influencers or influencees,each having their own influence score. Interactions between aninfluencer and other entity may comprise situations where the otherentity cites an opinion of the influencer, or forwards, or expressessupport for the opinion of the influencer. At 604, an interaction factoris calculated to take into account the amount of interactions with otherentities, as well as take into account the influence scores of the otherentities. At 606, sub-scores for each influencer with respect to atleast one data source are determined. The at least data source maycomprise different forms of sources from which opinions are gathered,such as news websites, Twitter, other social networks, or blogs. Thus,each discrete data source corresponds with its own sub-score for eachparticular influencer. At 608, the final influence score for eachinfluencer is generated based on the interaction factor and thesub-scores. Generation of the scores is explained above at least withrespect to Equations 1, 2, 3, and 4, which detail how the interactionfactor and various sub-scores are used to arrive at a final influencescore for any particular influencer. The final influence score for eachinfluencer may be stored at a database for later retrieval via a userinterface. The user interface may also be configured to provideadditional information to the user about particular influencers andpresent the list of influence scores in different formats at thedirection of the user. The user interface may reside at a user machineor reside at a machine networked with influence engine 110 and beaccessible from a user machine. The list of influence scores andinfluencer names may be stored at a database for retrieval via the userinterface based on the aforementioned steps of the process illustratedby FIG. 5 which are carried out by influence engine 110, or a computeror a processor embodying influence engine 110.

FIG. 7 illustrates an exemplary influence engine, in accordance with anembodiment of the present disclosure. Influence engine 110, as depictedby FIG. 7, shows the various components which may be a part of influenceengine 110 to carry out the determination of influence scores and thegeneration of influence scores. Influence engine 110, as depicted,comprises an opinion unit 702, influencer mapping unit 704, scoregeneration unit 706, and user interface unit 708. Opinion unit 702 isconfigured for retrieving opinions. The opinions are retrieved fromopinion extraction engine 106 (not shown), and retrieval is based on aselected topic. The selected topic may be selected by a user via a userinterface provided by user interface unit 708. Through the userinterface, the user selects a topic for opinions, including otherconstraints which may further define the topic. Once selected, theselected topic and constraints are provided by user interface unit 708to opinion unit 702 such that opinion unit 702 requests the opinionscorresponding to the selected topic and constraints from opinionextraction engine 106.

Influencer mapping unit 704 is configured for determining influencerseach associated with at least one opinion of the extracted opinions. Theopinions may be analyzed to determine those associated with a particularinfluencer. In the event that there are two or more influencers that areactually the same entity or are a common influencer, influencer mappingunit 704 by way of a mapping feature, may associate the opinions of thetwo or more influencers with the common influencer such that when theinfluence score is calculated, all of a common influencer's names,alias', and nicknames are taken into account.

Score generation unit 706 is configured for generating a score for eachinfluencer. The score is related to the selected topic as it is basedonly on opinions of the influencer regarding the selected topic.Calculation of the score, as described above, is based upon interactionsby the influencer with other different entities. It is also based onwhat data source the opinions originate from, and thus calculation ofthe score may be weighted based upon a weighting assigned to each datasource. Further description of score calculations is described above atleast with respect to Equations 1, 2, 3, and 4.

User interface unit 708 is configured for providing the influence scoresand influencer names to the user that selected the selected topic. Userinterface unit 708 provides this information through the aforementioneduser interface.

The above-described embodiments for collecting and analyzing applicationusage can be implemented on a computer using well-known computerprocessors, memory units, storage devices, computer software, and othercomponents. A high-level block diagram of such a computer is illustratedin FIG. 8. Computer 800 contains ns a processor 810, which controls theoverall operation of the computer 800 by executing computer programinstructions, which define such operations. The computer programinstructions may be stored in a storage device 820, or other computerreadable medium (e.g., magnetic disk, CD ROM, etc.), and loaded intomemory 830 when execution of the computer program instructions isdesired. Thus, any of the processes described herein can be defined bythe computer program instructions stored in the memory 830 and/orstorage 820 and controlled by the processor 810 executing the computerprogram instructions. For example, the computer program instructions canbe implemented as computer executable code programmed by one skilled inthe art to determine influencers and generate influence scores.Accordingly, by executing the computer program instructions, theprocessor 810 executes an algorithm for generating influence scores asdescribed herein. Computer 800 may also perform other functionalities,such as those described above in connection with all FIGS. correspondingto the embodiments described herein. The computer 800 also includes oneor more network interfaces 840 for communicating with other devices viaa network. The computer 800 also includes input/output devices 850 thatenable user interaction with the computer 800 (e.g., display, keyboard,mouse, speakers, buttons, etc.) One skilled in the art will recognizethat an implementation of an actual computer could contain othercomponents as well, and that FIG. 8 is a high level representation ofsome of the components of such a computer for illustrative purposes.

While the foregoing has described what are considered to be the bestmode and/or other examples, it is understood that various modificationsmay be made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that the teachings may beapplied in numerous applications, only some of which have been describedherein. It is intended by the following claims to claim and allapplications, modifications and variations that fall within the truescope of the present teachings.

1. A method implemented using a processor and a database, comprising:retrieving a plurality of opinions of a selected topic from thedatabase; determining influencers based on at least one thresholdparameter, each influencer associated with at least one opinion from theplurality of opinions, using the processor; and automatically generatinga score for each influencer using the processor, the score related tothe selected topic.
 2. The method of claim 1, wherein retrieving aplurality of opinions of a selected topic comprises: extracting opinionsfrom data sources.
 3. The method of claim 2, wherein the data sourcescomprise at least one of: news sites, blogs, forums, transcripts,tweets, comments, quotes, and social network entries.
 4. The method ofclaim 1, wherein determining influencers comprises: analyzing theopinions to determine those associated with an influencer; andassociating the opinions associated with the least one influenceridentities with the influencer.
 5. The method of claim 1, whereingenerating a score for each influencer comprises: determininginteractions by each influencer with other entities; calculating aninteraction factor for each influencer based on the interactions; andgenerating the score for each influencer based on the calculatedinteraction factor.
 6. The method of claim 1, wherein the score is basedon a weighted score of each influencer with respect to a data source. 7.The method of claim 1, further comprising: receiving the selected topicincluding a set of constraints.
 8. The method of claim 7, wherein theset of constraints includes at least one of: keywords, date limits,definitions, and product types.
 9. The method of claim 1, furthercomprising: providing for display, a list of each influencer includingthe corresponding score of each influencer with respect to the selectedtopic.
 10. The method of claim 1, wherein generating a score for eachinfluencer comprises: generating a score based on at least one of:quality of publications where the at least one opinion is found, anumber of search engine hits for the at least one opinion, a number ofmentions of the at least one opinion through social network entries, anddiversity of sources for the at least one opinion.
 11. The method ofclaim 1, wherein generating a score for each influencer comprises:generating a score based on a plurality of weighted sub-scores.
 12. Themethod of claim 1, wherein the selected topic may represent at least oneof: consumer goods, political issues, current events issues, and brandnames.
 13. The method of claim 1, wherein the score represents a unifiedscore based on a plurality of sub-scores each associated with a datasource.
 14. The method of claim 1, wherein retrieving a plurality ofopinions of a selected topic from the database comprises: retrieving aplurality of opinions associated with a predetermined time period. 15.The method of claim 14, wherein the predetermined time period is a rangeof 30 through 90 days.
 16. The method of claim 1, further comprising:automatically generating, based on the score related to the selectedtopic, at least one of: CPM values, details relating to demographics,and details relating to data sources.
 17. A system comprising: adatabase, a processor, and an influence engine comprising: an opinionunit configured for retrieving a plurality of opinions of a selectedtopic from the database; an influencer mapping unit configureddetermining influencers based on at least one threshold parameter, eachinfluencer associated with at least one opinion from the plurality ofopinions; and a score generation unit configured for automaticallygenerating a score for each influencer using the processor, the scorerelated to the selected topic.
 18. The system of claim 17, furthercomprising: an opinion extraction engine configured for extractingopinions from data sources, wherein the extracted opinions may beprovided to the opinion unit.
 19. The system of claim 18, wherein thedata sources comprise at least one of: news sites, blogs, forums,transcripts, tweets, comments, quotes, and social network entries. 20.The system of claim 17, wherein the influencer mapping unit is furtherconfigured for analyzing the opinions to determine those associated withan influencer; identifying at least two influencer identities thatrepresent the influencer; and associating the opinions associated withthe at least two influencer identities with the influencer.
 21. Thesystem of claim 17, wherein the score generation unit is furtherconfigured for determining interactions by each influencer with otherentities; calculating an interaction factor for each influencer based onthe interactions; and generating the score for each influencer based onthe calculated interaction factor.
 22. The system of claim 17, whereinthe score is based on a weighted score of each influencer with respectto a data source.
 23. The system of claim 17, further comprising: a userinterface unit configured for receiving the selected topic including aset of constraints.
 24. The system of claim 18, wherein the set ofconstraints includes at least one of: keywords, date limits, queries,definitions, and product types.
 25. The system of claim 17, furthercomprising: a user interface unit configured for providing for display alist of each influencer including the corresponding score of eachinfluencer with respect to the selected topic.
 26. The system of claim17, wherein the score generation unit is further configured forgenerating a score for each influencer based on at least one of: qualityof publications where the at least one opinion is found, a number ofsearch engine hits for the at least one opinion, a number of mentions ofthe at least one opinion through social network entries, and diversityof sources for the at least one opinion.
 27. The system of claim 17,wherein the score generation unit is further configured for generating ascore for each influencer based on a plurality of weighted sub-scores,29. The system of claim 17, wherein the selected topic may represent atleast one of: consumer goods, political issues, current events issues,and brand names.
 30. The system of claim 17, wherein the scorerepresents a unified score based on a plurality of sub-scores eachassociated with a data source.
 31. The system of claim 17, wherein theopinion unit is further configured for retrieving a plurality ofopinions associated with a predetermined time period.
 32. The system ofclaim 31, wherein the predetermined time period is a range of 30 through90 days.
 33. The system of claim 17, further comprising: a userinterface unit configured for: automatically generating, based on thescore related to the selected topic, at least one of: CPM values,details relating to, and details relating to data sources.
 34. A machinereadable non-transitory and tangible medium having information recordedthereon, wherein the information, when read by the machine, causes themachine to perform the steps comprising: retrieving a plurality ofopinions of a selected topic from a database; determining influencersbased on at least one threshold parameter, each influencer associatedwith at least one opinion from the plurality of opinions; andautomatically generating a score for each influencer using a processor,the score related to the selected topic.
 35. The machine readablenon-transitory and tangible medium of claim 34, wherein the informationfor retrieving a plurality of opinions of a selected topic, when read bythe machine, causes the machine to further perform the step of:extracting opinions from data sources.
 36. The machine readablenon-transitory and tangible medium of claim 34, wherein the data sourcescomprise at least one of: news sites, blogs, forums, transcripts,tweets, comments, quotes, and social network entries.
 37. The machinereadable non-transitory and tangible medium of claim 34, wherein theinformation for determining influencers each associated with at leastone opinion, when read by the machine, causes the machine to furtherperform the steps of: analyzing the opinions to determine thoseassociated with an influencer; identifying at least one influenceridentities that represent the influencer; and associating the opinionsassociated with the least one influencer identities with the influencer.38. The machine readable non-transitory and tangible medium of claim 34,wherein the information for generating a score for each influencer, whenread by the machine, causes the machine to further perform the steps of:determining interactions by each influencer with other entities;calculating an interaction factor for each influencer based on theinteractions; and generating the score for each influencer based on thecalculated interaction factor.
 39. The machine readable non-transitoryand tangible medium of claim 34, wherein the score is based on aweighted score of each influencer with respect to a data source.
 40. Themachine readable non-transitory and tangible medium of claim 34, whereinthe information, when read by the machine, causes the machine to furtherperform the step of: receiving the selected topic including a set ofconstraints.
 41. The machine readable non-transitory and tangible mediumof claim 40, wherein the set of constraints includes at least one of:keywords, date limits, queries, definitions, and product types.
 42. Themachine readable non-transitory and tangible medium of claim 34, whereinthe information, when read by the machine, causes the machine to furtherperform the step of: providing for display a list of each influencerincluding the corresponding score of each influencer with respect to theselected topic.
 43. The machine readable non-transitory and tangiblemedium of claim 34, wherein the information for generating a score foreach influencer, when read by the machine, causes the machine to furtherperform the step of: generating a score based on at least one of:quality of publications where the at least one opinion is found, anumber of search engine hits for the at least one opinion, a number ofmentions of the at least one opinion through social network entries, anddiversity of sources for the at least one opinion.
 44. The machinereadable non-transitory and tangible medium of claim 34, wherein theinformation for generating a score for each influencer, when read by themachine, causes the machine to further perform the step of: generating ascore based on a plurality of weighted sub-scores, each sub-scoreassociated with a period of time, wherein a score associated with ashorter period of time has a lower weighting factor than a scoreassociated with a longer period of time.
 45. The machine readablenon-transitory and tangible medium of claim 34, wherein the selectedtopic may represent at least one of: consumer goods, political issues,current events issues, and brand names.
 46. The machine readablenon-transitory and tangible medium of claim 34, wherein the scorerepresents a unified score based on a plurality of sub-scores eachassociated with a data source.
 47. The machine readable non-transitoryand tangible medium of claim 34, wherein the information for retrievinga plurality of opinions of a selected topic, when read by the machine,causes the machine to further perform the step of: retrieving aplurality of opinions associated with a predetermined time period. 48.The machine readable non-transitory and tangible medium of claim 47,wherein the predetermined time period is a range of 30 through 90 days.49. The machine readable non-transitory and tangible medium of claim 34,wherein the information, when read by the machine, causes the machine tofurther perform the step of: automatically generating, based on thescore related to the selected topic, at least one of: CPM values,details relating to demographics, and details relating to data sources.